There are different video object detection systems that facilitate the detection of events and activities of interest. Many different algorithms are frame based algorithms that require object detection at each frame and establishment of a correspondence between the object detections in consecutive frames to enable tracking; i.e., the frame and correspondence based tracking algorithms. The object correspondence between frames may be achieved by prediction filtering or particle filtering applied to object motion and appearance attributes.
Many prior art methods do not perform well with video collected in uncontrolled real world scenes, where a background image with no moving object maybe hard to obtain or a robust object kernel density or a boundary contour can not be established at each frame. Hence, there is still a need for an automatic video tracking system and method capable of robust performance in real life situations.